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Explain the concept of ensemble learning and provide a detailed comparison between bagging, boosting, and stacking. Discuss how each method improves model performance and handles overfitting.
A
Ensemble learning combines multiple models to improve overall performance. Bagging reduces variance, boosting reduces bias and variance, and stacking combines predictions from multiple models.
B
Ensemble learning uses a single model to improve performance. Bagging increases variance, boosting reduces bias, and stacking increases model complexity.
C
Ensemble learning is not effective in improving model performance. Bagging and boosting are similar, and stacking is used for reducing model complexity.
D
Ensemble learning combines multiple models to improve overall performance. Bagging reduces variance, boosting reduces bias, and stacking combines predictions from multiple models while handling overfitting.
